| About this Abstract | 
   
    | Meeting | 6th World Congress on Integrated Computational Materials Engineering (ICME 2022) | 
   
    | Symposium | 6th World Congress on Integrated Computational Materials Engineering (ICME 2022) | 
   
    | Presentation Title | Accelerated HEA Development and Evaluation via Combined Approach of Additive Manufacturing, Machine Learning, and Thermodynamic Modeling | 
   
    | Author(s) | Phalgun  Nelaturu, Jason  Hattrick-Simpers, Thien  Duong, Michael  Moorehead, Santanu  Chauduri, Adrien  Couet, Dan J Thoma | 
   
    | On-Site Speaker (Planned) | Phalgun  Nelaturu | 
   
    | Abstract Scope | Additive manufacturing via directed energy deposition was employed as a high-throughput technique to synthesize alloys in the Cr-Fe-Mn-Ni quaternary system. 120 alloy compositions were synthesized in a week, exploring a vast portion of the composition space. Tight compositional control within ±5 at% and <0.2% unmelted powder fraction were achieved. The rapid synthesis combined with rapid heat treatment, characterization, and nano- and micro-hardness measurements enabled high-throughput evaluation of these materials. The large dataset of experimentally measured properties were used to develop a predictive hardening model using active machine learning algorithm coupled with thermodynamic modeling. The overall high-throughput framework being developed for material discovery will be presented along with important insights into advantages of coupling blind machine learning with physics-based modeling. | 
   
    | Proceedings Inclusion? | Definite: Other |